Working papers results

This paper introduces Heckscher-Ohlin trade features into a two-country DSGE model, and studies the international transmission of productivity shocks through trade in goods. This framework improves upon existing international real business cycle models in generating business cycle properties comparable with the empirical evidence concerning the terms of trade and the trade balance.

Index tracking requires to build a portfolio of stocks (a replica) whose behavior is as close as possible to that of a given stock index. Typically, much fewer stocks should appear in the replica than in the index, and there should be no low frequency (persistent) components in the tracking error. Unfortunately, the latter property is not satisfied by many commonly used methods for index tracking. These are based on the in-sample minimization of a loss function, but do not take into account the dynamic properties of the index components. Instead, we represent the index components with a dynamic factor model, and develop a procedure that, in a first step, builds a replica that is driven by the same persistent factors as the index. In a second step, it is also possible to refine the replica so that it minimizes a loss function, as in the traditional approach. Both Monte Carlo simulations and an application to the EuroStoxx50 index provide substantial support for our approach.

Nowadays a considerable amount of information on the behavior of the economy is readily available, in the form of large datasets of macroeconomic variables. Central bankers can be expected to base their decisions on this very large information set, so that it can be difficult to track their decisions using small models, such as standard Taylor rules. Small scale structural VARs can suffer from a similar problem when used to highlight stylized facts or for policy simulation exercises. On the other hand, large scale structural models are hardly manageable, and still suffer from those identification problems that led to the success of VARs. In this paper we combine recent time-series techniques for the analysis of large datasets with more traditional small scale models to analyze monetary policy in Europe. In particular, we model hundreds of macroeconomic variables with a dynamic factor model, and summarize their informational content with a few estimated factors. These factors are then used as instruments in the estimation of forward looking Taylor rules, and as additional regressors in structural VARs. The latter are then used to evaluate the effects of unexpected and systematic monetary policy.

This paper studies the structure and time consistency of optimal monetary policy from a public finance perspective in an economy where agents di.er in preference for liquidity and holdings of nominal assets. I find that the presence of redistributional e.ects breaks the link between time consistency and high inflation which characterizes representative agent models of optimal fiscal and monetary policy. For a large class of economies, optimal monetary policy is time consistent. I relate these findings to key historical episodes of inflation and deflation.

This paper examines the price and quality choice by a single product risk-neutral monopolist who can delay irreversible investments required for market entry. It is shown that the price and quality she chooses at entry increase with uncertainty about the size of future demand. As opposed to a myopic monopolist she provides a quality that is socially optimal, but the moment at which she invests will be later than socially optimal. In a Stackelberg leader-follower game the leader pre-commits immediately regardless of the level of market uncertainty and may opt for the lower quality good rather than the higher quality good when market uncertainty is high.

When a foreign monopolist can either export to a host country or undertake an irreversible foreign direct investment (FDI), it is shown that the host government maximizes net domestic benefits by nearly fully subsidizing the investment cost in combination with taxing away benefits that exceed the gains from exporting. Since a higher tariff increases the firm's propensity to invest and increases tax benefits, maximizing net domestic benefits yields an optimal tariff that is higher than the one derived in previous studies that disregard the dynamics of FDI and the interaction between optimal tax and tariff policy.

This paper presents firm level evidence on the change of non-manual wage premia and employment shares in Italian manufacturing during the nineties. We find that the relative stability of aggregate wage premia and employment shares hides offsetting disaggregate forces. First, while technical progress raises the relative demand for skilled labor within firms, demand changes associated with exports reduce the relative demand for skills. Second, within the class of non-manual workers, wage premia and employment shares of executives rise substantially, whereas those of clerks fall in a similar proportion. We also find that the export status of firms plays a key role in explaining labor market dynamics, as exporters account for most of both demand-related and technology-related shifts. Overall, our results for Italy question the general validity of the conventional view that emphasizes the role of labor market institutions, as opposed to trade and technology, in determining wage and employment dynamics in continental Europe.

Time series models are often adopted for forecasting because of their simplicity and good performance. The number of parameters in these models increases quickly with the number of variables modelled, so that usually only univariate or small-scale multivariate models are considered. Yet, data are now readily available for a very large number of macroeconomic variables that are potentially useful when forecasting. Hence, in this paper we construct a large macroeconomic data-set for the UK, with about 80 variables, model it using a dynamic factor model, and compare the resulting forecasts with those from a set of standard time series models. We find that just six factors are sufficient to explain 50% of the variability of all the variables in the data set. Moreover, these factors, which can be considered as the main driving forces of the economy, are related to key variables such as interest rates, monetary aggregates, prices, housing and labour market variables, and stock prices. Finally, the factor-based forecasts are shown to improve upon standard benchmarks for prices, real aggregates, and financial variables, at virtually no additional modelling or computational costs.

This paper studies within-family decision making regarding investment in income protection for surviving spouses using a simple and tractable Nash-bargaining model. A change in US pension law (the Retirement Equity Act of 1984) is used as an instrument to derive predictions from the bargaining model and to contrast these with the predictions of the classical single-utility-function model of the household. This law change gave spouses of married pension-plan participants the right to survivor benefits unless they explicitly waived this right. The classical view of household behavior predicts that this would have had no effect on choices, while the bargaining model predicts an increase in spousal survivor protection. In the empirical part of the paper, the predictions of the classical model regarding the amount of life-insurance protection and the likelihood of a pensioner selecting survivor benefits are rejected in favor of the predictions of the Nash-bargaining model. The paper thus provides evidence for the need to take the existence of multiple decision makers into account when studying household behavior.

This paper investigates time series methods for forecasting four Euro-area wide aggregate variables: real GDP, industrial production, price inflation, and the unemployment rate. We consider two empirical questions arising from this problem. First, is it better to build aggregate Euro-area wide forecasting models for these variables, or are there gains from aggregating country-specific forecasts for the component country variables? Second, are there gains from using information from additional predictors beyond simple univariate time series forecasts, and if so, how large are these gains, and how are these gains best achieved? It turns out that typically there are gains from forecasting these series at the country level, then pooling the forecasts, relative to forecasting at the aggregate level. This suggests that structural macroeconometric modeling of the Euro area is appropriately done at the country-specific level, rather than directly at the aggregate level. Moreover, our simulated out-of-sample forecast experiment provides little evidence that forecasts from multivariate models are more accurate than forecasts from univariate models. If we restrict attention to multivariate models, the forecasts obtained from a dynamic factor model appear to be somewhat more accurate than the other methods.